We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Sparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar.
- Authors
Duan, Keqing; Wang, Zetao; Xie, Wenchong; Chen, Hui; Wang, Yongliang
- Abstract
To improve the performance of the recently developed parameter‐dependent sparse recovery (SR) space–time adaptive processing (STAP) algorithms in real‐world applications, the authors propose a novel clutter suppression algorithm with multiple measurement vectors (MMVs) using sparse Bayesian learning (SBL) strategy. First, the necessary and sufficient condition for uniqueness of sparse solutions to the SR STAP with MMV is derived. Then the SBL STAP algorithm in MMV case is introduced, and the process for hyperparameters estimation via expectation maximisation is given. Finally, a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithm are conducted. Results with both simulated and the Mountain‐Top data demonstrate the fast convergence and good performance of the proposed algorithm.
- Publication
IET Signal Processing (Wiley-Blackwell), 2017, Vol 11, Issue 5, p544
- ISSN
1751-9675
- Publication type
Article
- DOI
10.1049/iet-spr.2016.0183